{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Sensitivity analysis for a NetLogo model with SALib and ipyparallel\n",
    "\n",
    "This provides a more advanced example of interaction between NetLogo and a Python environment, using the [SALib library](https://salib.readthedocs.io/en/latest/) [(Herman & Usher, 2017)](https://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343); available through the pip package manager) to sample and analyze a suitable experimental design for a Sobol global sensitivity analysis. Furthermore, the [ipyparallel package (also available on pip)](http://ipyparallel.readthedocs.io/) is used to parallelize the simulations.\n",
    "\n",
    "All files used in the example are available from the pyNetLogo repository at https://github.com/quaquel/pyNetLogo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Ensuring compliance of code with both python2 and python3\n",
    "\n",
    "from __future__ import division, print_function\n",
    "try:\n",
    "    from itertools import izip as zip\n",
    "except ImportError: # will be 3.x series\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "sns.set_context('talk')\n",
    "\n",
    "import pyNetLogo\n",
    "\n",
    "#Import the sampling and analysis modules for a Sobol variance-based \n",
    "#sensitivity analysis\n",
    "from SALib.sample import saltelli\n",
    "from SALib.analyze import sobol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SALib relies on a problem definition dictionary which contains the number of input parameters to sample, their names (which should here correspond to a NetLogo global variable), and the sampling bounds. Documentation for SALib can be found at https://salib.readthedocs.io/en/latest/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "problem = { \n",
    "  'num_vars': 6,\n",
    "  'names': ['random-seed',\n",
    "            'grass-regrowth-time',\n",
    "            'sheep-gain-from-food',\n",
    "            'wolf-gain-from-food',\n",
    "            'sheep-reproduce',\n",
    "            'wolf-reproduce'], \n",
    "  'bounds': [[1, 100000],\n",
    "             [20., 40.], \n",
    "             [2., 8.], \n",
    "             [16., 32.],\n",
    "             [2., 8.],\n",
    "             [2., 8.]]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SALib sampler will automatically generate an appropriate number of samples for Sobol analysis, using a revised Saltelli sampling sequence. To calculate first-order, second-order and total sensitivity indices, this gives a sample size of _n(2p+2)_, where _p_ is the number of input parameters, and _n_ is a baseline sample size which should be large enough to stabilize the estimation of the indices. For this example, we use _n_ = 1000, for a total of 14000 experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 1000\n",
    "param_values = saltelli.sample(problem, n, calc_second_order=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampler generates an input array of shape (_n(2p+2)_, _p_) with rows for each experiment and columns for each input parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14000, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the experiments in parallel using ipyparallel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ipyparallel is a standalone package (available through the pip package manager) which can be used to interactively run parallel tasks from IPython on a single PC, but also on multiple computers. On machines with multiple cores, this can significantly improve performance: for instance, the multiple simulations required for a sensitivity analysis are easy to run in parallel. Documentation for Ipyparallel is available at http://ipyparallel.readthedocs.io/en/latest/intro.html.\n",
    "\n",
    "Ipyparallel first requires starting a controller and multiple engines, which can be done from a terminal or command prompt with the following:\n",
    "\n",
    "`ipcluster start -n 4` \n",
    "\n",
    "The optional -n argument specifies the number of processes to start (4 in this case).\n",
    "\n",
    "Next, we can connect the interactive notebook to the started cluster by instantiating a client, and checking that client.ids returns a list of 4 available engines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipyparallel\n",
    "\n",
    "client = ipyparallel.Client()\n",
    "client.ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then set up the engines so that they can run the simulations, using a \"direct view\" that accesses all engines.\n",
    "\n",
    "We first need to change the working directories to import pyNetLogo on the engines (assuming the pyNetLogo module is located in the same directory as this notebook, rather than being on the Python path). This also ensures we have the proper path to the file we need to load. We also send the SALib problem definition variable to the workspace of the engines, so that it can be used in the simulation.\n",
    "\n",
    "Note: there are various solutions to both problems. For example, we could make the NetLogo file a keyword argument and pass the absolute path to it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "direct_view = client[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AsyncResult: _push>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "#Push the current working directory of the notebook to a \"cwd\" variable on the engines that can be accessed later\n",
    "direct_view.push(dict(cwd=os.getcwd()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AsyncResult: _push>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Push the \"problem\" variable from the notebook to a corresponding variable on the engines\n",
    "direct_view.push(dict(problem=problem))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `%%px` command can be added to a notebook cell to run it in parallel on each of the engines. Here the code first involves some imports and a change of the working directory. We then start a link to NetLogo, and load the example model on each of the engines.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%px \n",
    "\n",
    "import os\n",
    "os.chdir(cwd)\n",
    "\n",
    "import pyNetLogo\n",
    "import pandas as pd\n",
    "\n",
    "netlogo = pyNetLogo.NetLogoLink(gui=False)\n",
    "netlogo.load_model('./models/Wolf Sheep Predation_v6.nlogo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use the IPyparallel map functionality to run the sampled experiments, now using a \"load balanced\" view to automatically handle the scheduling and distribution of the simulations across the engines. This is for instance useful when simulations may take different amounts of time.\n",
    "\n",
    "We first set up a simulation function that takes a single experiment (i.e. a vector of input parameters) as an argument, and returns the outcomes of interest in a pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def simulation(experiment):\n",
    "    \n",
    "    #Set the input parameters\n",
    "    for i, name in enumerate(problem['names']):\n",
    "        if name == 'random-seed':\n",
    "            #The NetLogo random seed requires a different syntax\n",
    "            netlogo.command('random-seed {}'.format(experiment[i]))\n",
    "        else:\n",
    "            #Otherwise, assume the input parameters are global variables\n",
    "            netlogo.command('set {0} {1}'.format(name, experiment[i]))\n",
    "\n",
    "    netlogo.command('setup')\n",
    "    #Run for 100 ticks and return the number of sheep and wolf agents at each time step\n",
    "    counts = netlogo.repeat_report(['count sheep','count wolves'], 100)    \n",
    "    \n",
    "    results = pd.Series([counts['count sheep'].values.mean(), \n",
    "                         counts['count wolves'].values.mean()], \n",
    "                         index=['Avg. sheep', 'Avg. wolves'])\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then create a load balanced view and run the simulation with the `map_sync` method. This method takes a function and a Python sequence as arguments, applies the function to each element of the sequence, and returns results once all computations are finished.\n",
    "\n",
    "In this case, we pass the simulation function and the array of experiments (param_values), so that the function will be executed for each row of the array.\n",
    "\n",
    "The DataFrame constructor is then used to immediately build a DataFrame from the results (which are returned as a list of Series). The `to_csv` method provides a simple way of saving the results to disk; pandas supports several more advanced storage options, such as serialization with msgpack, or hierarchical HDF5 storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lview = client.load_balanced_view()\n",
    "\n",
    "results = pd.DataFrame(lview.map_sync(simulation, param_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.to_csv('./data/Sobol_parallel.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. sheep</th>\n",
       "      <th>Avg. wolves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>125.25</td>\n",
       "      <td>91.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136.85</td>\n",
       "      <td>110.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125.98</td>\n",
       "      <td>84.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>136.46</td>\n",
       "      <td>106.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>284.34</td>\n",
       "      <td>55.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. sheep  Avg. wolves\n",
       "0      125.25        91.52\n",
       "1      136.85       110.37\n",
       "2      125.98        84.50\n",
       "3      136.46       106.76\n",
       "4      284.34        55.39"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SALib for sensitivity analysis\n",
    "\n",
    "We can then proceed with the analysis, first using a histogram to visualize output distributions for each outcome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,len(results.columns), sharey=True)\n",
    "\n",
    "for i, n in enumerate(results.columns):\n",
    "    ax[i].hist(results[n], 20)\n",
    "    ax[i].set_xlabel(n)\n",
    "ax[0].set_ylabel('Counts')\n",
    "\n",
    "fig.set_size_inches(10,4)\n",
    "fig.subplots_adjust(wspace=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bivariate scatter plots can be useful to visualize relationships between each input parameter and the outputs. Taking the outcome for the average sheep count as an example, we obtain the following, using the scipy library to calculate the Pearson correlation coefficient (r) for each parameter, and the seaborn library to plot a linear trend fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "nrow=2\n",
    "ncol=3\n",
    "\n",
    "fig, ax = plt.subplots(nrow, ncol, sharey=True)\n",
    "\n",
    "y = results['Avg. sheep']\n",
    "\n",
    "for i, a in enumerate(ax.flatten()):\n",
    "    x = param_values[:,i]\n",
    "    sns.regplot(x, y, ax=a, ci=None, color='k',scatter_kws={'alpha':0.2, 's':4, 'color':'gray'})\n",
    "    pearson = scipy.stats.pearsonr(x, y)\n",
    "    a.annotate(\"r: {:6.3f}\".format(pearson[0]), xy=(0.15, 0.85), xycoords='axes fraction',fontsize=13)\n",
    "    if divmod(i,ncol)[1]>0:\n",
    "        a.get_yaxis().set_visible(False)\n",
    "    a.set_xlabel(problem['names'][i])\n",
    "    a.set_ylim([0,1.1*np.max(y)])\n",
    "\n",
    "fig.set_size_inches(9,9,forward=True) \n",
    "fig.subplots_adjust(wspace=0.2, hspace=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates a positive relationship between the \"sheep-gain-from-food\" parameter and the mean sheep count, and negative relationships for the \"wolf-gain-from-food\" and \"wolf-reproduce\" parameters.\n",
    "\n",
    "We can then use SALib to calculate first-order (S1), second-order (S2) and total (ST) Sobol indices, to estimate each input's contribution to output variance as well as input interactions (again using the mean sheep count). By default, 95% confidence intervals are estimated for each index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Si = sobol.analyze(problem, results['Avg. sheep'].values, calc_second_order=True, print_to_console=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a simple example, we first select and visualize the total and first-order indices for each input, converting the dictionary returned by SALib to a DataFrame. The default pandas plotting method is then used to plot these indices along with their estimated confidence intervals (shown as error bars)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Si_filter = {k:Si[k] for k in ['ST','ST_conf','S1','S1_conf']}\n",
    "Si_df = pd.DataFrame(Si_filter, index=problem['names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ST</th>\n",
       "      <th>ST_conf</th>\n",
       "      <th>S1</th>\n",
       "      <th>S1_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random-seed</th>\n",
       "      <td>0.057656</td>\n",
       "      <td>0.007417</td>\n",
       "      <td>-0.001338</td>\n",
       "      <td>0.021451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grass-regrowth-time</th>\n",
       "      <td>0.099517</td>\n",
       "      <td>0.013123</td>\n",
       "      <td>0.044720</td>\n",
       "      <td>0.029019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sheep-gain-from-food</th>\n",
       "      <td>0.564394</td>\n",
       "      <td>0.059725</td>\n",
       "      <td>0.389392</td>\n",
       "      <td>0.073273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wolf-gain-from-food</th>\n",
       "      <td>0.233807</td>\n",
       "      <td>0.034540</td>\n",
       "      <td>0.145914</td>\n",
       "      <td>0.042733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sheep-reproduce</th>\n",
       "      <td>0.244354</td>\n",
       "      <td>0.046933</td>\n",
       "      <td>0.127663</td>\n",
       "      <td>0.049677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wolf-reproduce</th>\n",
       "      <td>0.232260</td>\n",
       "      <td>0.038720</td>\n",
       "      <td>0.122893</td>\n",
       "      <td>0.036159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ST   ST_conf        S1   S1_conf\n",
       "random-seed           0.057656  0.007417 -0.001338  0.021451\n",
       "grass-regrowth-time   0.099517  0.013123  0.044720  0.029019\n",
       "sheep-gain-from-food  0.564394  0.059725  0.389392  0.073273\n",
       "wolf-gain-from-food   0.233807  0.034540  0.145914  0.042733\n",
       "sheep-reproduce       0.244354  0.046933  0.127663  0.049677\n",
       "wolf-reproduce        0.232260  0.038720  0.122893  0.036159"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Si_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "indices = Si_df[['S1','ST']]\n",
    "err = Si_df[['S1_conf','ST_conf']]\n",
    "\n",
    "indices.plot.bar(yerr=err.values.T,ax=ax)\n",
    "fig.set_size_inches(8,4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"sheep-gain-from-food\" parameter has the highest ST index, indicating that it contributes over 50% of output variance when accounting for interactions with other parameters. However, it can be noted that confidence bounds are still quite broad with this sample size, particularly for the S1 index (which indicates each input's individual contribution to variance).\n",
    "\n",
    "We can use a more sophisticated visualization to include the second-order interactions between inputs estimated from the S2 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import itertools\n",
    "from math import pi\n",
    "\n",
    "\n",
    "def normalize(x, xmin, xmax):\n",
    "    return (x-xmin)/(xmax-xmin)\n",
    "\n",
    "\n",
    "def plot_circles(ax, locs, names, max_s, stats, smax, smin, fc, ec, lw, \n",
    "                 zorder):\n",
    "    s = np.asarray([stats[name] for name in names])\n",
    "    s = 0.01 + max_s * np.sqrt(normalize(s, smin, smax))\n",
    "    \n",
    "    fill = True\n",
    "    for loc, name, si in zip(locs, names, s):\n",
    "        if fc=='w':\n",
    "            fill=False\n",
    "        else:\n",
    "            ec='none'\n",
    "            \n",
    "        x = np.cos(loc)\n",
    "        y = np.sin(loc)\n",
    "        \n",
    "        circle = plt.Circle((x,y), radius=si, ec=ec, fc=fc, transform=ax.transData._b,\n",
    "                            zorder=zorder, lw=lw, fill=True)\n",
    "        ax.add_artist(circle)\n",
    "        \n",
    "\n",
    "def filter(sobol_indices, names, locs, criterion, threshold):\n",
    "    if criterion in ['ST', 'S1', 'S2']:\n",
    "        data = sobol_indices[criterion]\n",
    "        data = np.abs(data)\n",
    "        data = data.flatten() # flatten in case of S2\n",
    "        # TODO:: remove nans\n",
    "        \n",
    "        filtered = ([(name, locs[i]) for i, name in enumerate(names) if \n",
    "                     data[i]>threshold])\n",
    "        filtered_names, filtered_locs = zip(*filtered)\n",
    "    elif criterion in ['ST_conf', 'S1_conf', 'S2_conf']:\n",
    "        raise NotImplementedError\n",
    "    else:\n",
    "        raise ValueError('unknown value for criterion')\n",
    "\n",
    "    return filtered_names, filtered_locs\n",
    "\n",
    "\n",
    "def plot_sobol_indices(sobol_indices, criterion='ST', threshold=0.01):\n",
    "    '''plot sobol indices on a radial plot\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sobol_indices : dict\n",
    "                    the return from SAlib\n",
    "    criterion : {'ST', 'S1', 'S2', 'ST_conf', 'S1_conf', 'S2_conf'}, optional\n",
    "    threshold : float\n",
    "                only visualize variables with criterion larger than cutoff\n",
    "             \n",
    "    '''\n",
    "    max_linewidth_s2 = 15#25*1.8\n",
    "    max_s_radius = 0.3\n",
    "    \n",
    "    # prepare data\n",
    "    # use the absolute values of all the indices\n",
    "    #sobol_indices = {key:np.abs(stats) for key, stats in sobol_indices.items()}\n",
    "    \n",
    "    # dataframe with ST and S1\n",
    "    sobol_stats = {key:sobol_indices[key] for key in ['ST', 'S1']}\n",
    "    sobol_stats = pd.DataFrame(sobol_stats, index=problem['names'])\n",
    "\n",
    "    smax = sobol_stats.max().max()\n",
    "    smin = sobol_stats.min().min()\n",
    " \n",
    "    # dataframe with s2\n",
    "    s2 = pd.DataFrame(sobol_indices['S2'], index=problem['names'], \n",
    "                      columns=problem['names'])\n",
    "    s2[s2<0.0]=0. #Set negative values to 0 (artifact from small sample sizes)\n",
    "    s2max = s2.max().max()\n",
    "    s2min = s2.min().min()\n",
    "\n",
    "    names = problem['names']\n",
    "    n = len(names)\n",
    "    ticklocs = np.linspace(0, 2*pi, n+1)\n",
    "    locs = ticklocs[0:-1]\n",
    "\n",
    "    filtered_names, filtered_locs = filter(sobol_indices, names, locs,\n",
    "                                           criterion, threshold)\n",
    "    \n",
    "    # setup figure\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, polar=True)\n",
    "    ax.grid(False)\n",
    "    ax.spines['polar'].set_visible(False)\n",
    "    ax.set_xticks(ticklocs)\n",
    "\n",
    "    ax.set_xticklabels(names)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_ylim(top=1.4)\n",
    "    legend(ax)\n",
    "\n",
    "    # plot ST\n",
    "    plot_circles(ax, filtered_locs, filtered_names, max_s_radius, \n",
    "                 sobol_stats['ST'], smax, smin, 'w', 'k', 1, 9)\n",
    "\n",
    "    # plot S1\n",
    "    plot_circles(ax, filtered_locs, filtered_names, max_s_radius, \n",
    "                 sobol_stats['S1'], smax, smin, 'k', 'k', 1, 10)\n",
    "\n",
    "    # plot S2\n",
    "    for name1, name2 in itertools.combinations(zip(filtered_names, filtered_locs), 2):\n",
    "        name1, loc1 = name1\n",
    "        name2, loc2 = name2\n",
    "\n",
    "        weight = s2.loc[name1, name2]\n",
    "        lw = 0.5+max_linewidth_s2*normalize(weight, s2min, s2max)\n",
    "        ax.plot([loc1, loc2], [1,1], c='darkgray', lw=lw, zorder=1)\n",
    "\n",
    "    return fig\n",
    "\n",
    "\n",
    "from matplotlib.legend_handler import HandlerPatch\n",
    "class HandlerCircle(HandlerPatch):\n",
    "    def create_artists(self, legend, orig_handle,\n",
    "                       xdescent, ydescent, width, height, fontsize, trans):\n",
    "        center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent\n",
    "        p = plt.Circle(xy=center, radius=orig_handle.radius)\n",
    "        self.update_prop(p, orig_handle, legend)\n",
    "        p.set_transform(trans)\n",
    "        return [p]\n",
    "\n",
    "def legend(ax):\n",
    "    some_identifiers = [plt.Circle((0,0), radius=5, color='k', fill=False, lw=1),\n",
    "                        plt.Circle((0,0), radius=5, color='k', fill=True),\n",
    "                        plt.Line2D([0,0.5], [0,0.5], lw=8, color='darkgray')]\n",
    "    ax.legend(some_identifiers, ['ST', 'S1', 'S2'],\n",
    "              loc=(1,0.75), borderaxespad=0.1, mode='expand',\n",
    "              handler_map={plt.Circle: HandlerCircle()})\n",
    "\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig = plot_sobol_indices(Si, criterion='ST', threshold=0.005)\n",
    "fig.set_size_inches(7,7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the \"sheep-gain-from-food\" variable has strong interactions with the \"wolf-gain-from-food\" and \"wolf-reproduce\" inputs in particular. The size of the ST and S1 circles correspond to the normalized variable importances."
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
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   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
